Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "151" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 50 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 48 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459865 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 47.695216 | 4.957156 | 20.364059 | -0.330021 | 7.072673 | 5.627174 | 7.560690 | 0.286703 | 0.5719 | 0.6429 | 0.3539 | nan | nan |
| 2459864 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 55.939551 | 4.190685 | 10.361523 | 0.907088 | 2.981164 | 3.036385 | 1.940737 | 0.390234 | 0.5402 | 0.6153 | 0.4062 | nan | nan |
| 2459863 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 36.929414 | 1.442873 | -0.339581 | -0.952587 | 1.277775 | -0.464659 | 1.530405 | -0.716512 | 0.5326 | 0.6026 | 0.3909 | nan | nan |
| 2459862 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 37.763961 | 1.819456 | 13.117470 | 1.609247 | 8.197579 | 4.748641 | 1.886250 | -0.603061 | 0.5119 | 0.6371 | 0.4101 | nan | nan |
| 2459861 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 27.646081 | 0.386461 | -1.236930 | -0.706375 | 0.175001 | -1.500599 | 0.743926 | -0.427222 | 0.5565 | 0.6167 | 0.4060 | nan | nan |
| 2459860 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 28.343318 | 0.395440 | 9.929600 | 1.487337 | 4.876794 | 5.335082 | 0.226024 | -0.531567 | 0.5641 | 0.6276 | 0.4053 | nan | nan |
| 2459859 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 21.763301 | -0.035041 | -1.110973 | -0.810590 | 0.691338 | -1.877036 | 0.603676 | -0.486956 | 0.5929 | 0.6319 | 0.4062 | nan | nan |
| 2459858 | not_connected | 100.00% | 5.91% | 0.00% | 0.00% | 100.00% | 0.00% | 25.707814 | -0.163112 | -1.661940 | -0.932941 | 2.045383 | -2.056359 | 4.516063 | -0.353517 | 0.5868 | 0.6374 | 0.4149 | 2.286584 | 2.241503 |
| 2459857 | not_connected | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.190064 | 2.460088 | 1.804607 | 1.023222 | 0.383362 | -1.033489 | -0.890779 | -0.924075 | 0.0316 | 0.0295 | 0.0015 | nan | nan |
| 2459856 | not_connected | 100.00% | 1.14% | 0.00% | 0.00% | 100.00% | 0.00% | 34.595779 | 1.036030 | 9.555476 | 1.722802 | 3.767937 | 3.131353 | 0.905283 | -0.623953 | 0.5761 | 0.6576 | 0.4093 | 2.258954 | 2.267444 |
| 2459855 | not_connected | 100.00% | 11.49% | 0.00% | 0.00% | 100.00% | 0.00% | 44.113015 | 1.512859 | 10.244926 | 1.852762 | 0.823100 | 0.921064 | 1.455084 | -0.634441 | 0.5186 | 0.6666 | 0.4398 | 2.308530 | 2.202401 |
| 2459854 | not_connected | 100.00% | 8.06% | 0.00% | 0.00% | 100.00% | 0.00% | 45.542163 | 0.819379 | 8.910557 | 2.437037 | 1.638493 | 0.778870 | 0.380847 | -0.424219 | 0.5441 | 0.6931 | 0.4453 | 2.279651 | 2.269622 |
| 2459853 | not_connected | 100.00% | 7.31% | 0.00% | 0.00% | 100.00% | 0.00% | 31.688165 | 0.817660 | 11.249701 | 3.037308 | 2.479379 | 2.029976 | 2.549074 | -0.198550 | 0.5920 | 0.6452 | 0.4163 | 2.614475 | 2.465047 |
| 2459852 | not_connected | 100.00% | 0.54% | 0.00% | 0.00% | 100.00% | 0.00% | 6.074009 | 2.870741 | 2.417418 | 0.614273 | 140.391350 | 205.871546 | 399.061122 | 400.499977 | 0.7011 | 0.7990 | 0.2582 | 423.758858 | 113.973417 |
| 2459851 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 1.788812 | 6.503990 | -0.677876 | 1.518583 | 371.568173 | 481.077515 | 7011.270065 | 6681.696281 | nan | nan | nan | 0.000000 | 0.000000 |
| 2459850 | not_connected | 100.00% | 8.18% | 0.00% | 0.00% | 100.00% | 0.00% | 9.592991 | 2.954686 | 1.261755 | 1.526338 | 200.899389 | 232.353602 | 4751.510411 | 4938.256575 | 0.5936 | 0.7138 | 0.3538 | 375.475075 | 113.321886 |
| 2459849 | not_connected | 100.00% | 5.39% | 0.00% | 0.00% | 100.00% | 0.00% | 11.235656 | 2.350879 | 3.121760 | 3.763541 | 177.451138 | 209.324551 | 4713.125472 | 5054.200427 | 0.5908 | 0.7091 | 0.3656 | 417.110115 | 127.454418 |
| 2459848 | not_connected | 100.00% | 8.50% | 0.00% | 0.00% | 100.00% | 0.00% | 11.519703 | 2.273521 | 2.903326 | 3.027914 | 273.772357 | 319.502752 | 3909.600997 | 4170.537226 | 0.5526 | 0.7140 | 0.3909 | 2.680344 | 10.391122 |
| 2459847 | not_connected | 100.00% | 8.06% | 0.00% | 0.00% | 100.00% | 0.00% | 11.055431 | 2.709805 | 4.360295 | -0.561543 | 209.966914 | 294.430328 | 4639.813864 | 4572.016903 | 0.5739 | 0.6422 | 0.4182 | 732.352203 | 205.038872 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | ee Shape | 47.695216 | 47.695216 | 4.957156 | 20.364059 | -0.330021 | 7.072673 | 5.627174 | 7.560690 | 0.286703 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | ee Shape | 55.939551 | 4.190685 | 55.939551 | 0.907088 | 10.361523 | 3.036385 | 2.981164 | 0.390234 | 1.940737 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | ee Shape | 36.929414 | 36.929414 | 1.442873 | -0.339581 | -0.952587 | 1.277775 | -0.464659 | 1.530405 | -0.716512 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | ee Shape | 37.763961 | 37.763961 | 1.819456 | 13.117470 | 1.609247 | 8.197579 | 4.748641 | 1.886250 | -0.603061 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | ee Shape | 27.646081 | 0.386461 | 27.646081 | -0.706375 | -1.236930 | -1.500599 | 0.175001 | -0.427222 | 0.743926 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | ee Shape | 28.343318 | 28.343318 | 0.395440 | 9.929600 | 1.487337 | 4.876794 | 5.335082 | 0.226024 | -0.531567 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | ee Shape | 21.763301 | 21.763301 | -0.035041 | -1.110973 | -0.810590 | 0.691338 | -1.877036 | 0.603676 | -0.486956 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | ee Shape | 25.707814 | -0.163112 | 25.707814 | -0.932941 | -1.661940 | -2.056359 | 2.045383 | -0.353517 | 4.516063 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | nn Shape | 2.460088 | 2.460088 | 0.190064 | 1.023222 | 1.804607 | -1.033489 | 0.383362 | -0.924075 | -0.890779 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | ee Shape | 34.595779 | 34.595779 | 1.036030 | 9.555476 | 1.722802 | 3.767937 | 3.131353 | 0.905283 | -0.623953 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | ee Shape | 44.113015 | 1.512859 | 44.113015 | 1.852762 | 10.244926 | 0.921064 | 0.823100 | -0.634441 | 1.455084 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | ee Shape | 45.542163 | 0.819379 | 45.542163 | 2.437037 | 8.910557 | 0.778870 | 1.638493 | -0.424219 | 0.380847 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | ee Shape | 31.688165 | 0.817660 | 31.688165 | 3.037308 | 11.249701 | 2.029976 | 2.479379 | -0.198550 | 2.549074 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | nn Temporal Discontinuties | 400.499977 | 6.074009 | 2.870741 | 2.417418 | 0.614273 | 140.391350 | 205.871546 | 399.061122 | 400.499977 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | ee Temporal Discontinuties | 7011.270065 | 1.788812 | 6.503990 | -0.677876 | 1.518583 | 371.568173 | 481.077515 | 7011.270065 | 6681.696281 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | nn Temporal Discontinuties | 4938.256575 | 9.592991 | 2.954686 | 1.261755 | 1.526338 | 200.899389 | 232.353602 | 4751.510411 | 4938.256575 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | nn Temporal Discontinuties | 5054.200427 | 11.235656 | 2.350879 | 3.121760 | 3.763541 | 177.451138 | 209.324551 | 4713.125472 | 5054.200427 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | nn Temporal Discontinuties | 4170.537226 | 2.273521 | 11.519703 | 3.027914 | 2.903326 | 319.502752 | 273.772357 | 4170.537226 | 3909.600997 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151 | N16 | not_connected | ee Temporal Discontinuties | 4639.813864 | 2.709805 | 11.055431 | -0.561543 | 4.360295 | 294.430328 | 209.966914 | 4572.016903 | 4639.813864 |